China Intracranial Aneurysm Project (CIAP): protocol for a registry study on a multidimensional prediction model for

Junfan Chen1, Jian Liu1, Yisen Zhang1

  • 1Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China.

Insights

This study aims to develop a multidimensional model to predict the rupture risk of unruptured intracranial aneurysms (UIAs) using data from a large Chinese cohort. The model will integrate various factors to improve patient outcomes and inform treatment strategies for high-risk aneurysms.

Area of Science:

  • Neurosurgery
  • Radiology
  • Genetics
  • Biomarkers

Background:

  • Ruptured aneurysms causing subarachnoid hemorrhage have high mortality and morbidity.
  • Identified risk factors include smoking, hypertension, and female sex, but others like hemodynamics, imaging, and genomics are unclear.
  • No accurate predictive model for aneurysm rupture currently exists.

Purpose of the Study:

  • To develop a multidimensional model for predicting the rupture risk of unruptured intracranial aneurysms (UIAs).
  • To utilize data from a large Chinese cohort to establish an evidence-based prediction model.
  • To provide a theoretical basis for treating high-risk aneurysms.

Main Methods:

  • The China Intracranial Aneurysm Project-2 (CIAP-2) will screen 500 UIA patients from CIAP-1.
  • Data collection includes hemodynamic factors, high-resolution magnetic resonance imaging (HRMRI), genetic factors, and biomarkers.
  • Analysis of statistically significant clinical features from a 5000-patient UIA cohort (CIAP-1) will inform model development.

Main Results:

  • The study is designed to explore aneurysm rupture risk.
  • A multidimensional model for predicting UIA rupture risk will be developed.
  • The model will integrate diverse data including clinical features, imaging, genetics, and biomarkers.

Conclusions:

  • This research will advance the understanding of UIA rupture risk factors.
  • The developed model aims to improve the prediction of aneurysm rupture.
  • This provides a foundation for targeted treatment of high-risk intracranial aneurysms.
Abstract

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